Article: Analysis of spectrum sensing using convolutional neural network Journal: International Journal of Mobile Network Design and Innovation (IJMNDI) 2025 Vol.11 No.4 pp.226 - 235 Abstract: This study uses a spectrum sensing method to examine spectrum sensing as a classification problem. It works with the help of a convolutional neural network-long short-term memory (CM-LSTM) network and a signal correlation matrix. Three signals picked up by the space antenna array were cross- correlated with each other, and a single signal was picked up to find out the time. It was made with an LSTM (long short- term memory) network. The LSTM is a great way to find traits that have something to do with time. Then, a list of the signals picked up by the array and the links between them were given to the LSTM classification model. I figured out how the messages fit into the timeline and location. There were a lot more good ways to find bands after this. We studied using models and found that the CM-LSTM spectrum-sensing algorithm works better than the SVM, GBM, RF, and ED-based spectrum-sensing algorithms. Inderscience Publishers - linking academia, business and industry through research

Title: Analysis of spectrum sensing using convolutional neural network

Authors: Sankarsan Panda; D. Chitra Devi; M. Madhini; K. PeriyarSelvam

Addresses: Acharya Shri Mahapragya Institute of Excellence (ASMIE), Mahapragya Nagar, Asind, Bhilwara, Rajasthan, 311301, India ' Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, RMD College Rd., Kavaraipettai, Tamil Nadu, 601206, India ' Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, 600077, Tamil Nadu, India ' Department of ECE, GRT Institute of Engineering and Technology, GRT Mahalakshmi Nagar, Chennai-Tirupathi Highway, Tiruttani, Tiruvallur District, 631209, Tamil Nadu, India

Abstract: This study uses a spectrum sensing method to examine spectrum sensing as a classification problem. It works with the help of a convolutional neural network-long short-term memory (CM-LSTM) network and a signal correlation matrix. Three signals picked up by the space antenna array were cross- correlated with each other, and a single signal was picked up to find out the time. It was made with an LSTM (long short- term memory) network. The LSTM is a great way to find traits that have something to do with time. Then, a list of the signals picked up by the array and the links between them were given to the LSTM classification model. I figured out how the messages fit into the timeline and location. There were a lot more good ways to find bands after this. We studied using models and found that the CM-LSTM spectrum-sensing algorithm works better than the SVM, GBM, RF, and ED-based spectrum-sensing algorithms.

Keywords: CRN; cognitive radio network; CR; cognitive radio; SNR; signal-to-noise; matched filter detection (MFD); Ed; energy detection; CNN-LSTM; convolutional neural network-long short-term memory; ARQ; automatic repeat request.

DOI: 10.1504/IJMNDI.2025.151006

International Journal of Mobile Network Design and Innovation, 2025 Vol.11 No.4, pp.226 - 235

Received: 25 Feb 2025
Accepted: 07 May 2025
Published online: 08 Jan 2026 *

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Title: Analysis of spectrum sensing using convolutional neural network

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